An Infectious Disease Spread Simulation Based on Large Language Model Decision Making
Researchers have developed a new simulation framework that uses large language models (LLMs) to model individual decision-making during infectious disease outbreaks. This framework integrates LLM-generated decisions about self-reported illness into a synthetic population, considering factors like location, income, and education. The study simulated outcomes in San Francisco and Atlanta, finding that socioeconomic factors were the primary drivers of reporting rates, with geographical and message framing also playing roles. AI
IMPACT This framework could enhance public health interventions by providing more realistic simulations of disease spread influenced by human behavior.